How does Sampling affect a Spectrum?

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Sampling affects a signal's spectrum by repeating the frequency components at intervals determined by the sampling rate. When sampling below the Nyquist rate, aliasing occurs, resulting in unexpected frequency components, as seen in the example where frequencies shifted from 6000Hz to 4000Hz. Ideal sampling can be represented as a convolution of the signal's spectrum with Dirac deltas, while sample and hold introduces a sinc function to account for finite sampling time. Understanding these effects is crucial for accurately analyzing sampled signals. For further insights, consulting resources like "Communication Systems" by Simon Haykin is recommended.
frenzal_dude
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Hi, I tried sampling <br /> s(t)=sin(400\pi t) + 0.5cos(12000\pi t)<br /> <br /> at 10000Hz (below the Nyquist sampling rate)
in MATLAB and plotted the spectrum, I found that it had frequencies at +-200Hz (same as the continuous signal version) and +- 4000Hz(instead of 6000Hz in the continuous signal).

Just wondering how sampling actually affects the spectrum of the signal, is there a formula or something which can tell you the frequency components based on the sampling rate?

Thanks for your help,
frenzal
 
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Hi frenzal_dude,

Ideal Sampling (Sampling with an infinitesimally small window) is like convolving the signal in frequency domain by a series of Dirac Deltas. So you would just repeat the spectrum with a spacing = sampling frequency throughout the spectrum.

Sample and hold, also known as Zeroth order sampling, on the other hand is like the ideal sampling, but multuplied by a sinc function in the frequency domain, to account for the finite sampling time.

Let me know if you are having any problem with this. You can also take a look at "Communication Systems" by Simon Haykin; it has a very nice introduction to this topic.
 
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